{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# figs_instagram.ipynb\n",
    "\n",
    "Author: MB\n",
    "\n",
    "Date run: June 16, 2021\n",
    "\n",
    "Location: HPC\n",
    "\n",
    "The purpose of this script is to read in a dataset of retweets of Trump tweets and generate three figures\n",
    "* the cumulative number of retweets over time\n",
    "* the average number of followers per retweeter\n",
    "* the average number of engagements per retweeter\n",
    "\n",
    "Data in: \n",
    "* Decahose (filtered to just retweets of Trump tweets) -- 10% of all tweets\n",
    "* Dataset of interventions to tweets\n",
    "* labels for Trump tweets\n",
    "* crowdtangle posts containing Trump tweets - Instagram\n",
    "\n",
    "Data out: the aforementioned figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports \n",
    "import datetime\n",
    "import glob\n",
    "import json\n",
    "import os\n",
    "import warnings\n",
    "warnings.filterwarnings(action='ignore')\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fill_frame(frame, tweet_id):\n",
    "    '''\n",
    "    this function forward fills metrics to fill in missing minutes where there is not a record of the number of retweets\n",
    "    it then returns the filled dataframe\n",
    "    \n",
    "    :pd.DataFrame frame: a dataframe of tweets with the number of seconds since posting `seconds` and number of retweets at that timestamp `count`\n",
    "    '''\n",
    "    # sort dataframe in ascending order by seconds\n",
    "    frame = frame.sort_values('seconds')\n",
    "    \n",
    "    # append the missing seconds to the dataframe with an empty count value\n",
    "    seconds_we_have = frame['seconds'].unique()\n",
    "    frame['count'] = range(len(frame)) \n",
    "    frame['count'] = frame['count'] + 1\n",
    "    frame = frame.append([{'seconds':i} for i in range(0, 86401) if i not in seconds_we_have]).sort_values('seconds')\n",
    "    \n",
    "    # forward fill the dataframe (carry values up to the next non-null value)\n",
    "    frame['count'] = frame['count'].fillna(method='ffill')\n",
    "    frame['tweet_id'] = tweet_id\n",
    "    \n",
    "    return frame\n",
    "\n",
    "def convert_datetime(time):\n",
    "    '''\n",
    "    converts twitter timestamp string into a datetime object\n",
    "    \n",
    "    :str time: the twitter-style timestamp for tweet creation\n",
    "    :returns date: a datetime.datetime object corresponding to the time input\n",
    "    '''\n",
    "    return datetime.datetime.strptime(time, '%a %b %d %H:%M:%S +0000 %Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set colors\n",
    "no_intervention_color = '#16697a'\n",
    "soft_intervention_color = '#ffa62b'\n",
    "hard_intervention_color = '#721121'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in the intervention dataset\n",
    "interventions = pd.read_json('../data/interventions.json', orient='records', lines=True, dtype={'id_str':str})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in the labels and unique trump tweets \n",
    "labels = pd.read_csv('../data/labels.csv')\n",
    "trump_tweets = pd.read_csv('../data/trump_tweets.csv', dtype={'retweeted__id':str})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# copy `Final` data to `political` because I like that better\n",
    "trump_tweets['political'] = labels['Final']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter Trump tweets only to tweets labelled as political\n",
    "political_tweets = trump_tweets[trump_tweets['political'] == 'political']['retweeted__id'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter interventions to Trump tweets that are political\n",
    "interventions = interventions[interventions['id_str'].isin(political_tweets)].drop_duplicates('id_str').copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get tweet IDs by intervention type\n",
    "no_intervention = interventions[(interventions['hard_intervention'] == False) & (interventions['soft_intervention'] == False)]['id_str'].tolist()\n",
    "soft_intervention = interventions[(interventions['hard_intervention'] == False) & (interventions['soft_intervention'] == True)]['id_str'].tolist()\n",
    "hard_intervention = interventions[(interventions['hard_intervention'] == True)]['id_str'].tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Facebook Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in crowdtangle posts\n",
    "crowdtangle_posts = pd.read_json('../data/crowdtangle_posts-instagram.json', orient='records', lines=True,\n",
    "                                 dtype={'tweet_id':str})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cast crowdtangle date to utc\n",
    "crowdtangle_posts['date'] = crowdtangle_posts['date'].dt.tz_localize('UTC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter crowdtangle posts to those that we have intervention types for\n",
    "crowdtangle_posts = crowdtangle_posts[crowdtangle_posts['tweet_id'].isin(interventions['id_str'])].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge crowdtangle posts with the timestamp data from the tweet (on the tweet ID that matches them)\n",
    "fb_timestamps = pd.merge(crowdtangle_posts[['tweet_id', 'date']], interventions[['created_at', 'id_str']], \n",
    "                         left_on='tweet_id', right_on='id_str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter facebook posts to those that happened within 24 hours of the tweet publish date\n",
    "fb_timestamps = fb_timestamps[(fb_timestamps['date'] - fb_timestamps['created_at'] > datetime.timedelta(minutes=0))\n",
    "                              & (fb_timestamps['date'] - fb_timestamps['created_at'] <= datetime.timedelta(hours=24))\n",
    "                             ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calcuate the difference in time between the tweet publish date and the facebook post\n",
    "fb_timestamps['time_diff'] = (fb_timestamps['date'] - fb_timestamps['created_at']).values / np.timedelta64(1, 's')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create minutes and hours variables\n",
    "fb_timestamps['seconds'] = fb_timestamps['time_diff']\n",
    "fb_timestamps['minutes'] = fb_timestamps['seconds'] / 60\n",
    "fb_timestamps['hours'] = fb_timestamps['minutes'] / 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill each of the tweet frames to fill missing minutes\n",
    "filled_frames = pd.concat([fill_frame(frame, _) for _, frame in fb_timestamps.groupby('tweet_id')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split filled frames by intervention type\n",
    "hard = filled_frames[filled_frames['tweet_id'].isin(hard_intervention)].copy()\n",
    "soft = filled_frames[filled_frames['tweet_id'].isin(soft_intervention)].copy()\n",
    "none = filled_frames[filled_frames['tweet_id'].isin(no_intervention)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# take 1 hour rolling average of tweet counts\n",
    "hard_plot = hard.groupby('seconds')['count'].mean().rolling(window=3600, min_periods=20).mean()\n",
    "soft_plot = soft.groupby('seconds')['count'].mean().rolling(window=3600, min_periods=20).mean()\n",
    "none_plot = none.groupby('seconds')['count'].mean().rolling(window=3600, min_periods=20).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create growth figure\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.plot(hard_plot, label='Hard Intervention', color=hard_intervention_color, linewidth=4)\n",
    "plt.plot(soft_plot, label='Soft Intervention', color=soft_intervention_color, linewidth=4)\n",
    "plt.plot(none_plot, label='No Intervention', color=no_intervention_color, linewidth=4)\n",
    "\n",
    "plt.legend(fontsize=14)\n",
    "locs = [i*3600 for i in range(25)]\n",
    "plt.xticks(locs, range(25), rotation=45, fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.legend(fontsize=14)\n",
    "plt.xlabel('Time since tweet publication (hours)', fontsize=14)\n",
    "plt.ylabel('Number of Instagram Posts', fontsize=14)\n",
    "plt.title('Growth of Instagram Posts by Intervention', fontsize=14)\n",
    "plt.gca().spines['top'].set_visible(False)\n",
    "plt.gca().spines['right'].set_visible(False)\n",
    "plt.savefig('../plots/instagram_spread_of_tweets.png', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter crowdtangle posts by intervention type\n",
    "hard_ct = crowdtangle_posts[crowdtangle_posts['tweet_id'].isin(hard_intervention)].copy()\n",
    "soft_ct = crowdtangle_posts[crowdtangle_posts['tweet_id'].isin(soft_intervention)].copy()\n",
    "none_ct = crowdtangle_posts[crowdtangle_posts['tweet_id'].isin(no_intervention)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create average interaction figure\n",
    "plt.figure(figsize=(8, 10))\n",
    "\n",
    "plt.bar(0, width=.5, height=hard_ct.groupby('tweet_id')['total_statistics'].sum().mean(), color=hard_intervention_color, label='Hard Intervention')\n",
    "plt.bar(1, width=.5, height=soft_ct.groupby('tweet_id')['total_statistics'].sum().mean(), color=soft_intervention_color, label='Soft Itervention')\n",
    "plt.bar(2, width=.5, height=none_ct.groupby('tweet_id')['total_statistics'].sum().mean(), color=no_intervention_color, label='No Intervention')\n",
    "plt.legend(fontsize=14)\n",
    "plt.xticks([])\n",
    "plt.yticks(fontsize=14)\n",
    "plt.legend(fontsize=14, bbox_to_anchor = [1.5, .5])\n",
    "plt.xlabel('Intervention Type', fontsize=14)\n",
    "plt.ylabel('Average Total Interactions', fontsize=14)\n",
    "plt.title('Average Total Interactions by Intervention Type (Instagram)', fontsize=14)\n",
    "plt.gca().spines['top'].set_visible(False)\n",
    "plt.gca().spines['right'].set_visible(False)\n",
    "plt.savefig('../plots/instagram_avg_engagements.png', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
